

import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_			:					Optional[Any]    			=     'src/diffusers'
A_			:					Any    			=     '.'


# This is to make sure the diffusers module imported is the one in the repo.
A_			:					Tuple    			=     importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
A_			:					List[Any]    			=     spec.loader.load_module()

def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Optional[int]   )	-> Tuple:
	return line.startswith(a_   ) or len(a_   ) <= 1 or re.search(r"""^\s*\)(\s*->.*:|:)\s*$"""       , a_   ) is not None

def 		UpperCamelCase   (lowercase_:					Any   )	-> Union[str, Any]:
	A__				:       Optional[Any]	  =						object_name.split("""."""   )
	A__				:       Any	  =						0

	# First let's find the module where our object lives.
	A__				:       Tuple	  =						parts[i]
	while i < len(a_   ) and not os.path.isfile(os.path.join(a_       , f"""{module}.py"""   )   ):
		i += 1
		if i < len(a_   ):
			A__				:       Dict	  =						os.path.join(a_       , parts[i]   )
	if i >= len(a_   ):
		raise ValueError(f"""`object_name` should begin with the name of a module of diffusers but got {object_name}."""   )

	with open(os.path.join(a_       , f"""{module}.py"""   )       , """r"""       , encoding="""utf-8"""       , newline="""\n"""   ) as f:
		A__				:       Optional[Any]	  =						f.readlines()

	# Now let's find the class / func in the code!
	A__				:       str	  =						""
	A__				:       Any	  =						0
	for name in parts[i + 1 :]:
		while (
		    line_index < len(a_   ) and re.search(rf"""^{indent}(class|def)\s+{name}(\(|\:)"""       , lines[line_index]   ) is None
		):
			line_index += 1
		indent += "    "
		line_index += 1

	if line_index >= len(a_   ):
		raise ValueError(f""" {object_name} does not match any function or class in {module}."""   )

	# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
	A__				:       int	  =						line_index
	while line_index < len(a_   ) and _should_continue(lines[line_index]       , a_   ):
		line_index += 1
	# Clean up empty lines at the end (if any).
	while len(lines[line_index - 1]   ) <= 1:
		line_index -= 1

	A__				:       Dict	  =						lines[start_index:line_index]
	return "".join(a_   )


A_			:					Dict    			=     re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
A_			:					str    			=     re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
A_			:					int    			=     re.compile(r'<FILL\s+[^>]*>')

def 		UpperCamelCase   (lowercase_:					Optional[int]   )	-> List[str]:
	A__				:       Dict	  =						code.split("""\n"""   )
	A__				:       Optional[Any]	  =						0
	while idx < len(a_   ) and len(lines[idx]   ) == 0:
		idx += 1
	if idx < len(a_   ):
		return re.search(r"""^(\s*)\S"""       , lines[idx]   ).groups()[0]
	return ""

def 		UpperCamelCase   (lowercase_:					int   )	-> str:
	A__				:       int	  =						len(get_indent(a_   )   ) > 0
	if has_indent:
		A__				:       Dict	  =						f"""class Bla:\n{code}"""
	A__				:       Optional[int]	  =						black.Mode(target_versions={black.TargetVersion.PYaa}       , line_length=119       , preview=a_   )
	A__				:       int	  =						black.format_str(a_       , mode=a_   )
	A__				:       str	  =						style_docstrings_in_code(a_   )
	return result[len("""class Bla:\n"""   ) :] if has_indent else result

def 		UpperCamelCase   (lowercase_:					int       , lowercase_:					Optional[int]=False   )	-> Optional[Any]:
	with open(a_       , """r"""       , encoding="""utf-8"""       , newline="""\n"""   ) as f:
		A__				:       List[str]	  =						f.readlines()
	A__				:       Union[str, Any]	  =						[]
	A__				:       Optional[Any]	  =						0
	# Not a for loop cause `lines` is going to change (if `overwrite=True`).
	while line_index < len(a_   ):
		A__				:       Union[str, Any]	  =						_re_copy_warning.search(lines[line_index]   )
		if search is None:
			line_index += 1
			continue

		# There is some copied code here, let's retrieve the original.
		A__				:       Union[str, Any]	  =						search.groups()
		A__				:       Optional[Any]	  =						find_code_in_diffusers(a_   )
		A__				:       Union[str, Any]	  =						get_indent(a_   )

		A__				:       Union[str, Any]	  =						line_index + 1 if indent == theoretical_indent else line_index + 2
		A__				:       Any	  =						theoretical_indent
		A__				:       List[Any]	  =						start_index

		# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
		A__				:       int	  =						True
		while line_index < len(a_   ) and should_continue:
			line_index += 1
			if line_index >= len(a_   ):
				break
			A__				:       List[str]	  =						lines[line_index]
			A__				:       Optional[Any]	  =						_should_continue(a_       , a_   ) and re.search(f"""^{indent}# End copy"""       , a_   ) is None
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]   ) <= 1:
			line_index -= 1

		A__				:       Optional[int]	  =						lines[start_index:line_index]
		A__				:       Dict	  =						"".join(a_   )

		# Remove any nested `Copied from` comments to avoid circular copies
		A__				:       str	  =						[line for line in theoretical_code.split("""\n"""   ) if _re_copy_warning.search(a_   ) is None]
		A__				:       Optional[int]	  =						"\n".join(a_   )

		# Before comparing, use the `replace_pattern` on the original code.
		if len(a_   ) > 0:
			A__				:       Optional[int]	  =						replace_pattern.replace("""with"""       , """"""   ).split(""","""   )
			A__				:       int	  =						[_re_replace_pattern.search(a_   ) for p in patterns]
			for pattern in patterns:
				if pattern is None:
					continue
				A__				:       Dict	  =						pattern.groups()
				A__				:       Any	  =						re.sub(a_       , a_       , a_   )
				if option.strip() == "all-casing":
					A__				:       Tuple	  =						re.sub(obja.lower()       , obja.lower()       , a_   )
					A__				:       Dict	  =						re.sub(obja.upper()       , obja.upper()       , a_   )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
			A__				:       int	  =						blackify(lines[start_index - 1] + theoretical_code   )
			A__				:       List[str]	  =						theoretical_code[len(lines[start_index - 1]   ) :]

		# Test for a diff and act accordingly.
		if observed_code != theoretical_code:
			diffs.append([object_name, start_index]   )
			if overwrite:
				A__				:       Any	  =						lines[:start_index] + [theoretical_code] + lines[line_index:]
				A__				:       Dict	  =						start_index + 1

	if overwrite and len(a_   ) > 0:
		# Warn the user a file has been modified.
		print(f"""Detected changes, rewriting {filename}."""   )
		with open(a_       , """w"""       , encoding="""utf-8"""       , newline="""\n"""   ) as f:
			f.writelines(a_   )
	return diffs

def 		UpperCamelCase   (lowercase_:					bool = False   )	-> Optional[Any]:
	A__				:       Union[str, Any]	  =						glob.glob(os.path.join(a_       , """**/*.py"""   )       , recursive=a_   )
	A__				:       str	  =						[]
	for filename in all_files:
		A__				:       List[str]	  =						is_copy_consistent(a_       , a_   )
		diffs += [f"""- {filename}: copy does not match {d[0]} at line {d[1]}""" for d in new_diffs]
	if not overwrite and len(a_   ) > 0:
		A__				:       str	  =						"\n".join(a_   )
		raise Exception(
		    """Found the following copy inconsistencies:\n"""
		    + diff
		    + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""   )


if __name__ == "__main__":
	A_			:					Any    			=     argparse.ArgumentParser()
	parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
	A_			:					Optional[Any]    			=     parser.parse_args()

	check_copies(args.fix_and_overwrite)










'''simple docstring'''






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase									=   logging.get_logger(__name__)

lowerCAmelCase									=   {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class 		lowerCamelCase	(   _A							):
	snake_case_   = "sew-d"



	def __init__( self		,   a_=32		,   a_=768		,   a_=12		,   a_=12		,   a_=3_072		,   a_=2		,   a_=512		,   a_=256		,   a_=True		,   a_=True		,   a_=("p2c", "c2p")		,   a_="layer_norm"		,   a_="gelu_python"		,   a_=0.1		,   a_=0.1		,   a_=0.1		,   a_=0.0		,   a_=0.1		,   a_=0.02		,   a_=1e-7		,   a_=1e-5		,   a_="group"		,   a_="gelu"		,   a_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)		,   a_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)		,   a_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)		,   a_=False		,   a_=128		,   a_=16		,   a_=True		,   a_=0.05		,   a_=10		,   a_=2		,   a_=0.0		,   a_=10		,   a_=0		,   a_="mean"		,   a_=False		,   a_=False		,   a_=256		,   a_=0		,   a_=1		,   a_=2		,   **a_		,   ):
			super().__init__(**a_		,   pad_token_id=a_		,   bos_token_id=a_		,   eos_token_id=a_						)
			lowerCAmelCase  :     Optional[int]       						= hidden_size
			lowerCAmelCase  :     Optional[int]       						= feat_extract_norm
			lowerCAmelCase  :     str       						= feat_extract_activation
			lowerCAmelCase  :     Dict       						= list(a_						)
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     Any       						= list(a_						)
			lowerCAmelCase  :     Optional[Any]       						= conv_bias
			lowerCAmelCase  :     Tuple       						= num_conv_pos_embeddings
			lowerCAmelCase  :     int       						= num_conv_pos_embedding_groups
			lowerCAmelCase  :     Optional[int]       						= len(self.conv_dim						)
			lowerCAmelCase  :     Dict       						= num_hidden_layers
			lowerCAmelCase  :     str       						= intermediate_size
			lowerCAmelCase  :     Any       						= squeeze_factor
			lowerCAmelCase  :     Optional[int]       						= max_position_embeddings
			lowerCAmelCase  :     Tuple       						= position_buckets
			lowerCAmelCase  :     Union[str, Any]       						= share_att_key
			lowerCAmelCase  :     str       						= relative_attention
			lowerCAmelCase  :     List[str]       						= norm_rel_ebd
			lowerCAmelCase  :     int       						= list(a_						)
			lowerCAmelCase  :     str       						= hidden_act
			lowerCAmelCase  :     Dict       						= num_attention_heads
			lowerCAmelCase  :     Optional[Any]       						= hidden_dropout
			lowerCAmelCase  :     int       						= attention_dropout
			lowerCAmelCase  :     Optional[int]       						= activation_dropout
			lowerCAmelCase  :     Any       						= feat_proj_dropout
			lowerCAmelCase  :     Optional[int]       						= final_dropout
			lowerCAmelCase  :     Union[str, Any]       						= layer_norm_eps
			lowerCAmelCase  :     str       						= feature_layer_norm_eps
			lowerCAmelCase  :     Tuple       						= initializer_range
			lowerCAmelCase  :     int       						= vocab_size

			if (
			    (len(self.conv_stride						) != self.num_feat_extract_layers)
			    or (len(self.conv_kernel						) != self.num_feat_extract_layers)
			    or (len(self.conv_dim						) != self.num_feat_extract_layers)
			):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect."
					    "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
					    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
					    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

			# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
			lowerCAmelCase  :     List[str]       						= apply_spec_augment
			lowerCAmelCase  :     List[Any]       						= mask_time_prob
			lowerCAmelCase  :     Tuple       						= mask_time_length
			lowerCAmelCase  :     List[Any]       						= mask_time_min_masks
			lowerCAmelCase  :     List[str]       						= mask_feature_prob
			lowerCAmelCase  :     Union[str, Any]       						= mask_feature_length
			lowerCAmelCase  :     List[Any]       						= mask_feature_min_masks

			# ctc loss
			lowerCAmelCase  :     Any       						= ctc_loss_reduction
			lowerCAmelCase  :     str       						= ctc_zero_infinity

			# sequence classification
			lowerCAmelCase  :     Dict       						= use_weighted_layer_sum
			lowerCAmelCase  :     Tuple       						= classifier_proj_size





	@property
	def 	_lowerCamelCase     ( self						):
			return functools.reduce(operator.mul		,   self.conv_stride		,   1						)



